Characteristics of Surface EMG During Gait with and Without Power Assistance

Seiji Saito, Satoshi Muraki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Technology that assists and extends various functions of human beings will soon be available not only to medical and welfare but also to healthy individuals. This study aimed to characterize surface electromyography (EMG) signals in response to walking assistive equipment. Ten healthy male students walked on an 8-m uphill road (5.8% incline) using an assist walker (RT.2, RT.WORKS Co., Ltd) under assist and non-assist conditions. The EMG signals were recorded from four muscles (the rectus femoris [RF], biceps femoris, tibialis anterior, and lateral gastrocnemius). During loading response and terminal stance, the percent maximum voluntary isometric contraction (%MVC) peak value for RF was achieved more quickly in the assist condition than in the non-assist condition. However, during loading response and mid-swing, the %MVC peak value of RF was significantly lower in the assist condition than in the non-assist condition. These results indicate that humans alter muscle exertion patterns in specific muscles to adapt to walking assistance; such a change in the muscle exertion pattern may be adapted for smoother walking.

Original languageEnglish
Title of host publicationProceedings of the 20th Congress of the International Ergonomics Association (IEA 2018) - Volume II
Subtitle of host publicationSafety and Health, Slips, Trips and Falls
EditorsYushi Fujita, Sebastiano Bagnara, Thomas Alexander, Riccardo Tartaglia, Sara Albolino
PublisherSpringer Verlag
Pages739-743
Number of pages5
ISBN (Print)9783319960883
DOIs
Publication statusPublished - Jan 1 2019
Event20th Congress of the International Ergonomics Association, IEA 2018 - Florence, Italy
Duration: Aug 26 2018Aug 30 2018

Publication series

NameAdvances in Intelligent Systems and Computing
Volume819
ISSN (Print)2194-5357

Other

Other20th Congress of the International Ergonomics Association, IEA 2018
CountryItaly
CityFlorence
Period8/26/188/30/18

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Characteristics of Surface EMG During Gait with and Without Power Assistance'. Together they form a unique fingerprint.

  • Cite this

    Saito, S., & Muraki, S. (2019). Characteristics of Surface EMG During Gait with and Without Power Assistance. In Y. Fujita, S. Bagnara, T. Alexander, R. Tartaglia, & S. Albolino (Eds.), Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018) - Volume II: Safety and Health, Slips, Trips and Falls (pp. 739-743). (Advances in Intelligent Systems and Computing; Vol. 819). Springer Verlag. https://doi.org/10.1007/978-3-319-96089-0_80